Modelling_plots

Southern BC Spring and Fall fisheries

• The best model was one with a gamma distribution
• Next, we investigated if the fully interactive model is better than a subset of interactions. We used the dredge function from the MuMin package to sequentially drop terms (including all interactions).
• The best model included summer creel, marked and kept status, finescale fishery, and season and various interactions of these terms
• \[ catch = creel.summer+finescale.fishery+season+status+ creel.summer:finescale.fishery + creel.summer:status+ finescale.fishery:season \]

Show the code

#### Adding confidence intervals based on model to a dataframe for plotting purposes
#based on this blog: https://fromthebottomoftheheap.net/2018/12/10/confidence-intervals-for-glms/

family.set <- family(Spring_fall_model)
ilink.family.set<- family.set$linkinv

mod<-Spring_fall_model

ndata<-Season_south_no_nas %>% group_by(status, finescale_fishery_old, finescale_fishery, season) %>%  tidyr::expand(creel_plus_summer = seq(0, max(creel_plus_summer), length=100))
  

## add the fitted values by predicting from the model for the new data
ndata<- add_column(ndata, fit = predict(mod, newdata = ndata, type = 'response'))
ndata<- bind_cols(ndata, setNames(as_tibble(predict(mod, ndata, se.fit = TRUE)[1:2]),
                                                   c('fit_link','se_link')))

ndata <- mutate(ndata,
                fit_resp  = ilink.family.set(fit_link),
                right_upr = ilink.family.set(fit_link + (2 * se_link)),
                right_lwr = ilink.family.set(fit_link - (2 * se_link)))

Show the code

yearMonth2_season <- plyr::ddply(Sport_mark_rate_finescale, c( "YEAR", "MONTH", "finescale_fishery", "season"), summarise,
                         sum_creel = sum(creel, na.rm = TRUE), sum= sum(creel_plus, na.rm = TRUE), sum_historic= sum(historic_plus, na.rm = TRUE), sum_catch_estimate = sum(catch_estimate, na.rm = TRUE), sum_irec = sum(irec_calibrated, na.rm = TRUE)) %>% filter(finescale_fishery!="NA") %>% mutate(across(where(is.numeric), ~na_if(., 0))) 


yearMonth_catch_estimate_2_season<-yearMonth2_season %>% filter(sum_catch_estimate!=sum,sum_catch_estimate!=sum_historic, sum_catch_estimate!=sum_irec)

yearMonth_irec_2_season<-yearMonth2_season %>% filter(sum_catch_estimate==sum_irec)

CA JDF S FALL

CA JDF S SPRING

JNST S FALL

JNST S SPRING

NGS S FALL

NGS S SPRING

NWCVI S FALL AABM

NWCVI S SPRING AABM

SGS S FALL

SGS S SPRING

SWCVI S FALL AABM

SWCVI S SPRING AABM

Southern BC Summer fisheries

• The best model was one with a gamma distribution
• The best model included summer creel, marked and kept status, finescale fishery, and various interactions of these terms
• \[ catch = creel.summer+finescale.fishery+status+ creel.summer:finescale.fishery + creel.summer:status \]

Show the code

#### Adding confidence intervals based on model to a dataframe for plotting purposes
#based on this blog: https://fromthebottomoftheheap.net/2018/12/10/confidence-intervals-for-glms/

family.set <- family(Summer_model)
ilink.family.set<- family.set$linkinv

mod<-Summer_model

ndata<-Summer_south_no_nas %>% group_by(status, finescale_fishery_old, finescale_fishery, season) %>%  tidyr::expand(creel_plus_summer = seq(0, max(creel_plus_summer), length=100))
  

## add the fitted values by predicting from the model for the new data
ndata<- add_column(ndata, fit = predict(mod, newdata = ndata, type = 'response'))
ndata<- bind_cols(ndata, setNames(as_tibble(predict(mod, ndata, se.fit = TRUE)[1:2]),
                                                   c('fit_link','se_link')))

ndata <- mutate(ndata,
                fit_resp  = ilink.family.set(fit_link),
                right_upr = ilink.family.set(fit_link + (2 * se_link)),
                right_lwr = ilink.family.set(fit_link - (2 * se_link)))

CA JDF S SUMMER

JNST S SUMMER

NGS S SUMMER

NWCVI S SUMMER AABM

NWCVI S SUMMER ISBM

SGS S SUMMER

SWCVI S SUMMER AABM

SWCVI S SUMMER ISBM

NBC AABM Spring and Fall fisheries

• The best model was one with a gamma distribution
• The best model included summer creel, marked and kept status, and season and various interactions of these terms
• \[ catch = creel.summer+season+status + creel.summer:season \]

Show the code

#### Adding confidence intervals based on model to a dataframe for plotting purposes
#based on this blog: https://fromthebottomoftheheap.net/2018/12/10/confidence-intervals-for-glms/

family.set <- family(North_aabm_model)
ilink.family.set<- family.set$linkinv

mod<-North_aabm_model

ndata<-Season_north_aabm_no_nas %>% group_by(status, finescale_fishery_old, finescale_fishery, season) %>%  tidyr::expand(historic_summer = seq(0, max(historic_summer), length=100))


## add the fitted values by predicting from the model for the new data
ndata<- add_column(ndata, fit = predict(mod, newdata = ndata, type = 'response'))
ndata<- bind_cols(ndata, setNames(as_tibble(predict(mod, ndata, se.fit = TRUE)[1:2]),
                                                   c('fit_link','se_link')))

ndata <- mutate(ndata,
                fit_resp  = ilink.family.set(fit_link),
                right_upr = ilink.family.set(fit_link + (2 * se_link)),
                right_lwr = ilink.family.set(fit_link - (2 * se_link)))

NBC AABM S FALL

NBC AABM S SPRING

NBC AABM Summer fisheries

• The best model was one with a gamma distribution
• The best model included summer creel, marked and kept status, and season and various interactions of these terms

Show the code

#### Adding confidence intervals based on model to a dataframe for plotting purposes
#based on this blog: https://fromthebottomoftheheap.net/2018/12/10/confidence-intervals-for-glms/

family.set <- family(Summer_north_aabm_model_gamma_spec)
ilink.family.set<- family.set$linkinv

mod<-Summer_north_aabm_model_gamma_spec

ndata<-Summer_north_aabm_no_nas %>% group_by(status, finescale_fishery_old, finescale_fishery, season) %>%  tidyr::expand(historic_summer = seq(0, max(historic_summer), length=100))


## add the fitted values by predicting from the model for the new data
ndata<- add_column(ndata, fit = predict(mod, newdata = ndata, type = 'response'))
ndata<- bind_cols(ndata, setNames(as_tibble(predict(mod, ndata, se.fit = TRUE)[1:2]),
                                                   c('fit_link','se_link')))

ndata <- mutate(ndata,
                fit_resp  = ilink.family.set(fit_link),
                right_upr = ilink.family.set(fit_link + (2 * se_link)),
                right_lwr = ilink.family.set(fit_link - (2 * se_link)))

NBC AABM S SUMMER

NBC ISBM Spring and Fall fisheries

• The best model was one with a gamma distribution
• The best model included summer creel, marked and kept status, and season and various interactions of these terms
• \[ catch = creel.summer+season+status + season:status \]

Show the code

#### Adding confidence intervals based on model to a dataframe for plotting purposes
#based on this blog: https://fromthebottomoftheheap.net/2018/12/10/confidence-intervals-for-glms/

family.set <- family(nbc_isbm_model_full_gamma_spec)
ilink.family.set<- family.set$linkinv

mod<-nbc_isbm_model_full_gamma_spec

ndata<-Season_north_isbm_no_nas %>% group_by(status, finescale_fishery_old, finescale_fishery, season) %>%  tidyr::expand(historic_summer = seq(0, max(historic_summer), length=100))

## add the fitted values by predicting from the model for the new data
ndata<- add_column(ndata, fit = predict(mod, newdata = ndata, type = 'response'))
ndata<- bind_cols(ndata, setNames(as_tibble(predict(mod, ndata, se.fit = TRUE)[1:2]),
                                                   c('fit_link','se_link')))

ndata <- mutate(ndata,
                fit_resp  = ilink.family.set(fit_link),
                right_upr = ilink.family.set(fit_link + (2 * se_link)),
                right_lwr = ilink.family.set(fit_link - (2 * se_link)))

NBC ISBM S FALL

NBC ISBM S SPRING

NBC ISBM Summer fisheries

• The best model was one with a gamma distribution
• The best model included summer creel, marked and kept status, and season and various interactions of these terms

Show the code

#### Adding confidence intervals based on model to a dataframe for plotting purposes
#based on this blog: https://fromthebottomoftheheap.net/2018/12/10/confidence-intervals-for-glms/

family.set <- family(Summer_north_isbm_model_full_gamma_spec)
ilink.family.set<- family.set$linkinv

mod<-Summer_north_isbm_model_full_gamma_spec

ndata<-Summer_north_isbm_no_nas %>% group_by(status, finescale_fishery_old, finescale_fishery, season) %>%  tidyr::expand(historic_summer = seq(0, max(historic_summer), length=100))

## add the fitted values by predicting from the model for the new data
ndata<- add_column(ndata, fit = predict(mod, newdata = ndata, type = 'response'))
ndata<- bind_cols(ndata, setNames(as_tibble(predict(mod, ndata, se.fit = TRUE)[1:2]),
                                                   c('fit_link','se_link')))

ndata <- mutate(ndata,
                fit_resp  = ilink.family.set(fit_link),
                right_upr = ilink.family.set(fit_link + (2 * se_link)),
                right_lwr = ilink.family.set(fit_link - (2 * se_link)))

NBC ISBM S SUMMER

CBC ISBM fisheries

• The best model was one with a gamma distribution
• The best model included summer creel, marked and kept status, and season and various interactions of these terms
• \[ catch = creel.summer+status \]

Show the code

#### Adding confidence intervals based on model to a dataframe for plotting purposes
#based on this blog: https://fromthebottomoftheheap.net/2018/12/10/confidence-intervals-for-glms/

family.set <- family(cbc_isbm_model)
ilink.family.set<- family.set$linkinv

mod<-cbc_isbm_model

ndata<-Season_cbc_isbm_no_nas %>% group_by(status, finescale_fishery_old, finescale_fishery, season) %>%  tidyr::expand(historic_summer = seq(0, max(historic_summer), length=100))

## add the fitted values by predicting from the model for the new data
ndata<- add_column(ndata, fit = predict(mod, newdata = ndata, type = 'response'))
ndata<- bind_cols(ndata, setNames(as_tibble(predict(mod, ndata, se.fit = TRUE)[1:2]),
                                                   c('fit_link','se_link')))

ndata <- mutate(ndata,
                fit_resp  = ilink.family.set(fit_link),
                right_upr = ilink.family.set(fit_link + (2 * se_link)),
                right_lwr = ilink.family.set(fit_link - (2 * se_link)))

CBC S